{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2df5c18e",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e8012b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2b1c196",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成正态分布样本\n",
    "np.random.seed(42)  # 设置随机种子以便结果可重现\n",
    "mu, sigma = 0, 1    # 均值和标准差\n",
    "sample_size = 10000\n",
    "data = np.random.normal(mu, sigma, sample_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a1efe7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 步骤1：计算一阶原点矩（均值）\n",
    "mean = np.mean(data)\n",
    "\n",
    "# 步骤2：计算二阶中心矩（方差）\n",
    "variance = np.mean((data - mean)**2)\n",
    "std_dev = np.sqrt(variance)  # 标准差\n",
    "\n",
    "# 步骤3：计算三阶中心矩（偏度）\n",
    "skewness = np.mean((data - mean)**3) / (std_dev**3)\n",
    "\n",
    "# 步骤4：计算四阶中心矩（峰度）\n",
    "kurtosis = np.mean((data - mean)**4) / (std_dev**4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7dce6fcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用scipy库验证结果\n",
    "scipy_mean = stats.tmean(data)\n",
    "scipy_var = stats.tvar(data)\n",
    "scipy_skew = stats.skew(data)\n",
    "scipy_kurt = stats.kurtosis(data, fisher=False)  # fisher=False表示计算Pearson峰度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9fd0aec3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "样本统计量（n=10000）:\n",
      "均值: -0.0021 (scipy验证: -0.0021)\n",
      "方差: 1.0068 (scipy验证: 1.0069)\n",
      "偏度: 0.0020 (scipy验证: 0.0020)\n",
      "峰度: 3.0265 (scipy验证: 3.0265)\n"
     ]
    }
   ],
   "source": [
    "# 打印结果\n",
    "print(f\"样本统计量（n={sample_size}）:\")\n",
    "print(f\"均值: {mean:.4f} (scipy验证: {scipy_mean:.4f})\")\n",
    "print(f\"方差: {variance:.4f} (scipy验证: {scipy_var:.4f})\")\n",
    "print(f\"偏度: {skewness:.4f} (scipy验证: {scipy_skew:.4f})\")\n",
    "print(f\"峰度: {kurtosis:.4f} (scipy验证: {scipy_kurt:.4f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43e3b1c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27010 (\\N{CJK UNIFIED IDEOGRAPH-6982}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23494 (\\N{CJK UNIFIED IDEOGRAPH-5BC6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24230 (\\N{CJK UNIFIED IDEOGRAPH-5EA6}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27491 (\\N{CJK UNIFIED IDEOGRAPH-6B63}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24577 (\\N{CJK UNIFIED IDEOGRAPH-6001}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20998 (\\N{CJK UNIFIED IDEOGRAPH-5206}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24067 (\\N{CJK UNIFIED IDEOGRAPH-5E03}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26679 (\\N{CJK UNIFIED IDEOGRAPH-6837}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26412 (\\N{CJK UNIFIED IDEOGRAPH-672C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 19982 (\\N{CJK UNIFIED IDEOGRAPH-4E0E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29702 (\\N{CJK UNIFIED IDEOGRAPH-7406}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35770 (\\N{CJK UNIFIED IDEOGRAPH-8BBA}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "g:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20540 (\\N{CJK UNIFIED IDEOGRAPH-503C}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化数据分布\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(data, bins=50, density=True, alpha=0.6, color='g')\n",
    "\n",
    "# 绘制理论正态分布曲线\n",
    "xmin, xmax = plt.xlim()\n",
    "x = np.linspace(xmin, xmax, 100)\n",
    "p = stats.norm.pdf(x, mu, sigma)\n",
    "plt.plot(x, p, 'k', linewidth=2)\n",
    "\n",
    "plt.title('正态分布样本与理论分布')\n",
    "plt.xlabel('值')\n",
    "plt.ylabel('概率密度')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "my_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
